Uma Proposta de Rastreio de Objetos Utilizando Computação em Borda
Resumo
Muito se tem avançado nos campos da ciência e tecnologia, as pessoas cercam-se por sistema computacionais, os quais as auxiliam em suas rotinas. O presente artigo, apresenta os resultados do desenvolvimento de uma abordagem para detectar e rastrear pessoas na borda. O sistema é executado em uma placa Raspberry Pi 3B e, com apoio de uma câmera monitora o ambiente. Construiu-se uma interface que avalia o fluxo de pessoas que utilizam uma determinada escada de alvenaria. Informações como: calorias gastas, esforço ou tempo de atividade são oferecidos aos usuários em contrapartida pelo uso das escadas.
Palavras-chave:
Computação em Borda, Rastreio de Pessoas, Cálculos Calóricos
Referências
Pi NoIR Camera v2. https://www.raspberrypi.org/products/pi-noir-camera-v2/. Acesso: 20/03/2020.
Raspberry Pi model description. https://www.raspberrypi.org/products/raspberry-pi-3-model-b/. Acesso: 20/03/2020.
Brisolara, L. and Matos, J. (2009). Desafios no Projeto de Sistemas Embarcados, pages 153–175.
Forsyth, D. A. and Ponce, J. (2002). Computer vision: a modern approach. Prentice Hall Professional Technical Reference.
Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M. (2013). Internet of things (iot): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):1645–1660.
Heath, S. (2002). Embedded systems design. Elsevier.
Knappmeyer, M., Kiani, S. L., Reetz, E. S., Baker, N., and Tonjes, R. (2013). Sur- vey of context provisioning middleware. IEEE Communications Surveys & Tutorials, 15(3):1492–1519.
Lee, C.-C., Wang, C.-Y., Kao, C.-W., and Fan, K.-C. (2019). Real-time embedded system for human detection and tracking. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), pages 147–148. The Steering Committee of The World Congress in Computer Science, Computer.
Minichino, J. and Howse, J. (2020). Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning. Packt Publishing Ltd.
Nelson, D. L. and Cox, M. M. (2018). Princ ́ıpios de Bioqu ́ımica de Lehninger-7. Artmed Editora.
Østby, T. G. (2018). Object detection and tracking on a raspberry pi using background subtraction and convolutional neural networks. Master’s thesis, Universitetet i Sørøst- Norge.
Pimentel, V. and Nickerson, B. G. (2012). Communicating and displaying real-time data with websocket. IEEE Internet Computing, 16(4):45–53.
Remizov, A. (1991). Física médica y biológica. Mir.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91–99.
Riener, R., Rabuffetti, M., and Frigo, C. (2002). Stair ascent and descent at different inclinations. Gait & posture, 15(1):32–44.
Rose, J. and GAMBLE, J. G. (1998). Marcha humana. São Paulo: Premier.
Tepljakov, A. (2015). Raspberry Pi based System for Visual Object Detection and Trac-king. PhD thesis, Bachelor’s Thesis.
Wolf, M. (2012). Computers as components: principles of embedded computing system design. Elsevier.
Raspberry Pi model description. https://www.raspberrypi.org/products/raspberry-pi-3-model-b/. Acesso: 20/03/2020.
Brisolara, L. and Matos, J. (2009). Desafios no Projeto de Sistemas Embarcados, pages 153–175.
Forsyth, D. A. and Ponce, J. (2002). Computer vision: a modern approach. Prentice Hall Professional Technical Reference.
Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M. (2013). Internet of things (iot): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):1645–1660.
Heath, S. (2002). Embedded systems design. Elsevier.
Knappmeyer, M., Kiani, S. L., Reetz, E. S., Baker, N., and Tonjes, R. (2013). Sur- vey of context provisioning middleware. IEEE Communications Surveys & Tutorials, 15(3):1492–1519.
Lee, C.-C., Wang, C.-Y., Kao, C.-W., and Fan, K.-C. (2019). Real-time embedded system for human detection and tracking. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), pages 147–148. The Steering Committee of The World Congress in Computer Science, Computer.
Minichino, J. and Howse, J. (2020). Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning. Packt Publishing Ltd.
Nelson, D. L. and Cox, M. M. (2018). Princ ́ıpios de Bioqu ́ımica de Lehninger-7. Artmed Editora.
Østby, T. G. (2018). Object detection and tracking on a raspberry pi using background subtraction and convolutional neural networks. Master’s thesis, Universitetet i Sørøst- Norge.
Pimentel, V. and Nickerson, B. G. (2012). Communicating and displaying real-time data with websocket. IEEE Internet Computing, 16(4):45–53.
Remizov, A. (1991). Física médica y biológica. Mir.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91–99.
Riener, R., Rabuffetti, M., and Frigo, C. (2002). Stair ascent and descent at different inclinations. Gait & posture, 15(1):32–44.
Rose, J. and GAMBLE, J. G. (1998). Marcha humana. São Paulo: Premier.
Tepljakov, A. (2015). Raspberry Pi based System for Visual Object Detection and Trac-king. PhD thesis, Bachelor’s Thesis.
Wolf, M. (2012). Computers as components: principles of embedded computing system design. Elsevier.
Publicado
30/06/2020
Como Citar
OLIVEIRA, Fernando; DELATORRE, Mateus; REINSTEIN, Heberth.
Uma Proposta de Rastreio de Objetos Utilizando Computação em Borda. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 12. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 101-110.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2020.11216.
